Prompt Engineering in Clinical Practice: Tutorial for Clinicians.

Area of Science:

• Artificial Intelligence in Medicine
• Clinical Informatics
• Natural Language Processing

Background:

• Large language models (LLMs) offer transformative potential in healthcare, impacting clinical decision-making, patient communication, and administrative efficiency.
• Effective utilization of LLMs is critically dependent on prompt design, posing challenges for clinicians lacking expertise in natural language processing (NLP).

Purpose of the Study:

• To provide a comprehensive tutorial on prompt engineering techniques specifically tailored for clinical applications of LLMs.
• To equip clinicians with actionable strategies for leveraging LLMs to enhance healthcare delivery.

Main Methods:

• Exploration of various prompt engineering methods, including zero-shot, one-shot, few-shot, chain-of-thought, self-consistency, generated knowledge, and meta-prompting.
• Guidance on defining objectives, applying core principles of prompt design, and iterative refinement processes.
• Strategies for integrating LLM applications into interoperable electronic health record (EHR) systems.

Main Results:

• Clinicians can significantly improve LLM output quality through strategic prompt engineering.
• The tutorial offers a structured framework for applying advanced prompting techniques in clinical settings.
• Implementation guidance facilitates the integration of LLMs into existing healthcare workflows.

Conclusions:

• Prompt engineering is essential for maximizing the benefits of LLMs in healthcare.
• This framework empowers clinicians to utilize LLMs for improved decision-making, documentation, and patient engagement.
• Adherence to ethical standards and patient safety is paramount in the clinical application of LLMs.